Spectrum-based feature localization: A case study using ArgoUML: A case study using ArgoUML

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dc.contributor.authorMichelon, Gabriela K.
dc.contributor.authorSotto-Mayor, Bruno
dc.contributor.authorMartinez, Jabier
dc.contributor.authorArrieta, Aitor
dc.contributor.authorAbreu, Rui
dc.contributor.authorAssunção, Wesley K. G.
dc.contributor.editorMousavi, Mohammad
dc.contributor.editorSchobbens, Pierre-Yves
dc.contributor.editorAraujo, Hugo
dc.contributor.editorSchaefer, Ina
dc.contributor.editorter Beek, Maurice H.
dc.contributor.editorDevroey, Xavier
dc.contributor.editorRojas, Jose Miguel
dc.contributor.editorPinto, Monica
dc.contributor.editorTeixeira, Leopoldo
dc.contributor.editorBerger, Thorsten
dc.contributor.editorNoppen, Johannes
dc.contributor.editorReinhartz-Berger, Iris
dc.contributor.editorTemple, Paul
dc.contributor.editorDamiani, Ferruccio
dc.contributor.editorPetke, Justyna
dc.contributor.institutionSWT
dc.date.issued2021-09-06
dc.descriptionPublisher Copyright: © 2021 ACM.
dc.description.abstractFeature localization (FL) is a basic activity in re-engineering legacy systems into software product lines. In this work, we explore the use of the Spectrum-based localization technique for this task. This technique is traditionally used for fault localization but with practical applications in other tasks like the dynamic FL approach that we propose. The ArgoUML SPL benchmark is used as a case study and we compare it with a previous hybrid (static and dynamic) approach from which we reuse the manual and testing execution traces of the features. We conclude that it is feasible and sound to use the Spectrum-based approach providing promising results in the benchmark metrics.en
dc.description.statusPeer reviewed
dc.format.extent5
dc.format.extent606356
dc.identifier.citationMichelon , G K , Sotto-Mayor , B , Martinez , J , Arrieta , A , Abreu , R & Assunção , W K G 2021 , Spectrum-based feature localization: A case study using ArgoUML : A case study using ArgoUML . in M Mousavi , P-Y Schobbens , H Araujo , I Schaefer , M H ter Beek , X Devroey , J M Rojas , M Pinto , L Teixeira , T Berger , J Noppen , I Reinhartz-Berger , P Temple , F Damiani & J Petke (eds) , unknown . vol. F171624-A , Part F171624-A , Association for Computing Machinery , pp. 126-130 , 25th ACM International Systems and Software Product Line Conference, SPLC 2021 , Virtual, Online , United Kingdom , 6/09/21 . https://doi.org/10.1145/3461001.3473065
dc.identifier.citationconference
dc.identifier.doi10.1145/3461001.3473065
dc.identifier.isbn9781450384698
dc.identifier.otherresearchoutputwizard: 11556/1207
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85115345131&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofunknown
dc.relation.ispartofseriesPart F171624-A
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsArgoUML SPL benchmark
dc.subject.keywordsDynamic feature localization
dc.subject.keywordsSpectrum-based localization
dc.subject.keywordsArgoUML SPL benchmark
dc.subject.keywordsDynamic feature localization
dc.subject.keywordsSpectrum-based localization
dc.subject.keywordsSoftware
dc.subject.keywordsHuman-Computer Interaction
dc.subject.keywordsComputer Vision and Pattern Recognition
dc.subject.keywordsComputer Networks and Communications
dc.titleSpectrum-based feature localization: A case study using ArgoUML: A case study using ArgoUMLen
dc.typeconference output
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